Method for monitoring and/or controlling one or more chemical plant(s)

ABSTRACT

Disclosed is a method for monitoring and/or controlling a chemical plant (12) with multiple assets via a distributed computing system (10) with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise at least two of a first processing layer (14), a second processing layer (16, 32, 34) and an external processing layer (30), the method comprising the steps of: providing (60) a containerized application (48, 50) including an asset or plant template specifying input data, output data and an asset or plant model, deploying (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the chemical plant (12), providing (66) the generated output data for controlling and/or monitoring the chemical plant (12).

FIELD

The disclosure relates to a method for monitoring and/or controlling achemical plant with multiple assets via a distributed computing systemwith multiple deployment layers.

BACKGROUND

Chemical production is a highly sensitive production environmentparticularly with respect to safety. Chemical plants typically includemultiple assets to produce the chemical product. Multiple sensors aredistributed in such plants for monitoring and control purposes andcollect masses of data. As such chemical production is a data heavyenvironment. However, to date the gain from such data to increaseproduction efficiency in one or multiple chemical plants has not beenfully leveraged.

Applying new technologies in cloud computing and big data analytics ishence of great interest. Unlike other manufacturing industries, however,process industry is subject to very high safety standards. For thisreason, computing infrastructures are typically siloed with highlyrestrictive access to monitoring and control systems. Owing to suchsafety standards, latency and availability considerations contravene asimple migration of to date embedded control systems to e.g. a cloudcomputing system. Bridging the gap between highly proprietary industrialmanufacturing systems and cloud technologies is one of the majorchallenges.

WO2016065493 discloses a client device and a system for data acquisitionand pre-processing of process-related mass data from at least one CNCmachine or an industrial robot and for transmitting said process-relateddata to at least one data recipient, e.g. a cloud-based server isdescribed. The client device comprises at least one first datacommunication interface to at least one controller of the CNC machine orindustrial robot, for continuously recording hard-realtimeprocess-related data via at least one realtime data channel, and forrecording non-realtime process-related data via at least onenon-realtime data channel. The client device further comprises at leastone data processing unit data-mapping at least the recorded non-realtimedata to the recorded hard-realtime data to aggregate a contextualizedset of process-related data. Moreover, the client device comprises atleast one second data interface for transmitting the contextualized setof process-related data to the data recipient and for further datacommunication with the data recipient.

WO2019138120 discloses a method for improving a chemical productionprocess. A plurality of derivative chemical products are producedthrough a derivative chemical production process based on at least somederivative process parameters at a respective chemical productionfacility, which chemical production facilities each comprises a separaterespective facility intranet. At least some respective derivativeprocess parameters are measured from the derivative chemical productionprocess by a respective production sensor computer system within eachfacility intranet. A process model for simulating the derivativechemical production process is recorded in a process model managementcomputer system outside the facility intranets.

US20160320768A1 discloses an example network environment for monitoringplant processes with system computers operating as a root-causeanalyzer. The system computers communicate with the data server toaccess collected data for measurable process variables from a historiandatabase. The data server is communicatively coupled to a distributedcontrol system (DCS) in turn communicating collected data to the dataserver over communications network.

The object of the present invention relates to a highly scalable andflexible method for monitoring and/or controlling chemical plants inprocess industry, which adheres to the high safety standards and allowsfor enhanced monitoring or controlling.

SUMMARY

A method for monitoring and/or controlling a chemical plant withmultiple assets via a distributed computing system with more than twodeployment layers is proposed. The deployment layers comprise at leasttwo of a first processing layer, a second processing layer and anexternal processing layer. The method comprises the steps of:

-   -   providing a containerized application including an asset or        plant template specifying input data, output data and an asset        or plant model,    -   deploying the containerized application to execute on at least        one of the deployment layers, wherein the assignment of the        deployment layer depends on the input data, a load indicator, or        a system layer tag, and executing the containerized application        on the respective deployment layer to generate output data for        controlling and/or monitoring the chemical plant,    -   providing the generated output data for controlling and/or        monitoring the chemical plant.

A system for monitoring and/or controlling a chemical plant withmultiple assets with more than two deployment layers is proposed,wherein the deployment layers comprise at least two of a firstprocessing layer, a second processing layer and an external processinglayer, the system being configured to:

-   -   provide a containerized application including an asset or plant        template specifying input data, output data and an asset or        plant model,    -   deploy the containerized application to execute on at least one        of the deployment layers, wherein the deployment layer is        assigned based on the input data, a load indicator, or a system        layer tag, and executing the containerized application on the        assigned deployment layer(s) to generate output data for        controlling and/or monitoring the chemical plant,    -   provide the generated output data for controlling and/or        monitoring the chemical plant.

The present invention further relates to a distributed computer programor computer program product with computer-readable instructions that,when executed on one or more processor(s), cause the processor(s) toperform the methods for monitoring and/or controlling one or morechemical plant(s) as described herein. The invention further relates toa computer readable non-volatile or non-transitory storage medium withcomputer-readable instructions that, when executed on one or more aprocessor(s), cause the processor(s) to perform the methods formonitoring and/or controlling one or more chemical plant(s) as describedherein.

The proposed method allows for highly efficient application handling ina distributed computing system controlling and/or monitoring chemicalplants. By introducing different process and storage layers, the massdata transfer, orchestration and execution of applications can bedistributed over different layers allowing for flexible applicationhandling. Moreover, the concept of three system layers allows for highlyavailable and secure monitoring and/or controlling, since the secondprocessing and the external management layer are redundant. In otherwords, more critical tasks may be assigned to on premise computingresources not relying on an external network, while less critical tasksmay be assigned to external computing resources. An additional advantageis that based on the context the method enables an automation for thedeployment of applications and the automatic identification of needs forretro-fit of additional sensors or IOT sensors.

Additionally, the proposed method can accommodate multiple chemicalplants via the second processing layer or the external processing layer.Hence the method enables a highly scalable application orchestration formore reliable and enhanced monitoring and/or controlling of chemicalplants. In particular, orchestration of containerized application in adiverse application landscape can be organized to adhere to the specificneeds of process industry. For instance, the deployment of containerizedapplications ingesting input data can be streamlined for multiple assetseven in multiple plants. Additionally, depending on the specific datarequired by the containerized application and the computing resourcesrequired to run such applications, the appropriate processing layer maybe chosen, thus adhering to high availability standards in chemicalplants. For instance, computationally heavy applications ingesting plantspecific may be executed on the second processing layer, while processapplications ingesting asset or process specific data and requiring lowlatency may be executed on the first processing layer. Further criteriawhen to orchestrate on which deployment layer may be defined.

The following description relates to the system, the method, thecomputer program, the computer readable storage medium lined out above.In particular the systems, the input units, the computer programs andthe computer readable storage media are configured to perform the methodsteps as set out above and further described below.

In the context of the present invention chemical plant refers to anymanufacturing facility based on chemical processes, e.g. transforming afeedstock to a product using chemical processes. In contrast to discretemanufacturing, chemical manufacturing is based on continuous or batchprocesses. As such monitoring and/or controlling of chemical plants istime dependent and hence based on large time series data sets. Achemical plant may include more than 1.000 sensors producing measurementdata points every couple of seconds. Such dimensions result in multipleterabytes of data to be handled in a system for controlling and/ormonitoring chemical plants. A small-scale chemical plant may include acouple of thousand sensors producing data points every 1 to 10 s. Forcomparison a large-scale chemical plant may include a couple often-thousand sensors, e.g. 10.000 to 30.000, producing data points every1 to 10 s. Contextualizing such data results in the handling of multiplehundred gigabytes to multiple terabytes.

Chemical plants may produce a product via one or more chemical processestransforming the feedstock via one or more intermediate products to theproduct. Preferably a chemical plant provides an encapsulated facilityproducing a product, that may be used as feedstock for the next steps inthe value chain. Chemical plants may be large-scale plants like oil andgas facilities, gas cleaning plants, carbon dioxide capture facilities,liquefied natural gas (LNG) plants, oil refineries, petro-chemicalfacilities or chemical facilities. Upstream chemical plants inpetro-chemicals process production for example include a steamcrackerstarting with naphtha being processed to ethylene and propylene. Theseupstream products may then be provided to further chemical plants toderive downstream products such as polyethylene or polypropylene, whichmay again serve as feedstock for chemical plants deriving furtherdownstream products. Chemical plants may be used to manufacture discreteproducts. In one example one chemical plant may be used to manufactureprecursors for polyurethane foam. Such precursors may be provided to asecond chemical plant for the manufacture of discrete products, such asan isolation plate comprising polyurethane foam.

The value chain production via various intermediate products to an endproduct can be decentralized in various locations or integrated in aVerbund site or a chemical park. Such Verbund sites or chemical parkscomprise a network of interconnected chemical plants, where productsmanufactured in one plant can serve as a feedstock for another plant.

Chemical plants may include multiple assets, such as heat exchangers,reactors, pumps, pipes, distillation or absorption columns to name a fewof them. In chemical plants some assets may be critical. Critical assetsare those, which when disrupted critically impact plant operation. Thiscan lead to manufacturing processes being compromised. Reduced productquality or even manufacturing stops may the result. In the worst-casescenario fire, explosion or toxic gas release may be the result of suchdisruption. Hence such critical assets may require more rigorousmonitoring and/or controlling then other assets depending on thechemical processes and the chemicals involved. To monitor and/or controlchemical processes and assets multiple actors and sensors may beembedded in the chemical plant. Such actors or sensors may provideprocess or asset specific data relating to e.g. the state of anindividual asset, the state of an individual actor, the composition of achemical, or the state of a chemical process. In particular, process orasset specific data include one or more of the following datacategories:

-   -   process operation data, such as composition of a feedstock or an        intermediate product,    -   process monitoring data, such as flow, material temperature,    -   asset operation data, such as current, voltage, and    -   asset monitoring data, such as asset temperature, asset        pressure, vibrations.

In the context of the present disclosure assets may include anycomponent of the chemical plant, such as equipment, instrumentation,machine, process or process component. Hence, an asset model may relateto a machine, equipment, instrumentation, process or process componentmodel.

Process or asset specific data refers to data relating to a specificasset or process and contextualized with respect to such specific assetor process. Process or asset specific data may be contextualized onlywith respect to individual assets and processes. Process or assetspecific data may include measurement value, data quality measure, time,measurement unit, asset identifier for specific assets or processidentifier for a specific process sections or stages. Such process orasset specific data may be collected on the lowest processing layer orthe first processing layer and contextualized with respect to specificassets or processes in a single plant. Such contextualization may relateto context available on the first processing layer. Such context mayrelate to a single plant.

Plant specific data refers to process or asset specific data that iscontextualized with respect to one or more plant(s). Such plant specificdata may be collected on the second processing layer and contextualizedwith respect to multiple plants. Specifically, contextualization mayrelate to context available on the second processing layer. Viacontextualization context such as plant identifier, plant type,reliability indicator, or alarm limits for the plant may be added toprocess or asset specific data points. In a further step technical assetstructure of one or more plant(s), a Verbund site or a chemical park,other asset management structure (e.g. asset network), or applicationcontext (e.g. model identifier, third party exchange) may be added. Suchoverarching context can originate from functional locations or digitaltwins, such as digital piping and instrumentation diagrams, 3D models orscans with xyz coordinates of the plant assets. Additionally oralternatively local scans from mobile devices linked to e.g. piping andinstrumentation diagrams may be used for contextualization.

In particular, plant specific data relating to interfaces betweenchemical plants in a manufacturing chain may be provided on the secondprocessing layer or the external processing layer. Thus, monitoringand/or controlling, e.g. via anomaly detection, setpoint steering andoptimization in chains across multiple plants, can be enhanced. Formonitoring and/or controlling the chain across multiple plants processapplications with online in/out data profiles may be used. Such data andprocess applications may be transferred between plants. Combined withmass and energy balances that can be monitored, such processapplications may optimize the full chain across chemical plants ratherthan individual plants in the chain

The process of contextualization refers to linking data points availablein one or more storage unit(s). Such unit(s) may be persistent ornon-volatile storage. Data points may relate to measurement values orcontext information. Storage unit(s) may be part of the first processinglayer, the second processing layer, the external processing layer ordistributed across two or more of those layers. The linking may begenerated dynamically or statically. E.g. pre-defined or dynamicallygenerated scripts may generate dynamic or static links betweeninformation data points in one processing layer or across processinglayers. Links may be established by generating a new data objectincluding the linked data itself and storing such new data object in anew instance. Any data point stored may be actively deleted, if a copyis stored elsewhere. Any data point thus copied from one storage unit toa new data object in the same or another storage unit may be deleted toreduce storage space. Additionally, or alternatively links may beestablished by generating a meta data object with embedded links toaddress or access respective data points in distributed storage unit(s).Any data point thus addressable or accessible through the meta dataobject may remain in its original storage unit. Linking such informationto form a new data object may still be performed e.g. on the externalprocessing layer. For the retrieval of data either data objects areaccessed directly or meta data objects are used to address or access thedata distributed in one or more storage unit(s). Any operations on suchdata such as applications may either access such data directly, mayaccess a non-persistent image of such data, e.g. from cache memory, or apersistent copy of the data.

In the present context a containerized application refers to a processapplication which may be executed in an encapsulated runtime environmentindependent of a host's operating system. The application may hence beviewed to run in a sandbox. The containerized application may be basedon a container image containing the application. The container image mayinclude software components, e.g. hierarchical tree of softwarecomponents, required to execute the respective application in anencapsulated runtime environment. Such containerized application may bestored in a registry of or associated with the second processing layeror the external processing layer.

To deploy a containerized application an orchestration applicationassociated with the second processing layer or the external processinglayer may manage execution of the containerized application. Suchmanagement may include general runtime environment configurations suchas storage or network to run the containerized application. Suchmanagement may further include a host assignment defining a centralmaster node or a distribution among one or more computing node(s) toexecute the application on the first processing layer, the secondprocessing layer or the external management layer. In particular suchassignment of computing resources depends on the input data, the loadindicator, or the system layer tag.

The input data may include real time data from sensors, such as wirelessmonitoring devices or IoT devices, non-real time data, or output data ofdeployed containers or executed applications. Such data may relate tomachinery, such as machinery type or sensor data measured with respectto the machinery, chemicals, such as chemicals type or sensor datameasured with respect to chemical components processed in the chemicalplant, processes, such as chemical process type or sensor data measuredwith respect to the chemical processes performed in the chemical plant,and/or plant, such as plant type or sensor data measured with respect tothe chemical plant, e.g. environmental measurement data.

The asset or plant model may include a data-driven or a kinetic modelproviding e.g. a health status, an operation forecast, an event forecastor an event trigger. The asset or plant model may be based on a meredata-driven model, a hybrid model combining data-driven and kineticmodels or a mere kinetic model. The asset or plant model may further bebased on a scenario matrix mapping input data, e.g. sensor data, tospecific events. The asset model may reflect the physical behavior of asingle or multiple asset(s). The plant model may reflect the physicalbehavior of parts of one or more plant(s), a full plant or multipleplants.

The output data may include key performance indicators relating to theasset, the plant, the input data, an asset model performance or a plantmodel performance. Asset model performance or a plant model performancemay be embedded in the asset or plant model hosted by the containerizedapplication. Any generated output data of the method may be used in aone or more further containerized applications as input data. This waychains of containerized applications may be realized to build a systemof system coverage and use the generated output data for controllingand/or monitoring one or multiple chemical plants. Such chemical plantmay be parallel manufacturing plant or plants connected along the valuechain.

The containerized application may include one or more operations toingest input data, provide the input data to respective asset or plantmodels to generate output data and to provide the generated output datafor controlling and/or monitoring the chemical plant. Such output datamay be passed to a persistent instance after execution of theapplication. In particular such output data may be passed to acontrolling instance, e.g. on the first processing layer of the chemicalplant. Additionally or alternatively such output data may be passed to amonitoring instance on the first processing layer, the second processinglayer or the external processing layer. The output data may be passed toe.g. a client application for display to an operator or a furthercontainerized application for execution.

In one aspect the second processing layer includes larger storage andcomputing resources than the first processing layer. Alternatively oradditionally the external processing layer may include larger storageand computing resources than the second processing layer. Such stackedresource structure aids to bridge the gap between embedded controlsystems of chemical plants and available cloud technologies. Inparticular, embedded control systems of chemical plants are limited instorage and processing capabilities. Extending such resources allows forenhanced monitoring and/or control.

The first and the second processing layer may be hosted, situated,configured in or inside a secure network. The first processing layer maybe communicatively coupled to the second processing layer. The firstprocessing layer may include at least one core process system associatedwith the chemical plant or a single chemical plant. Preferably the firstprocessing layer is configured to control and/or monitor chemicalprocesses and assets on the asset level in individual plants. Hence thefirst processing layer monitors and/or controls the chemical plant onthe lowest level. Further preferred the first processing layer isconfigured to monitor and control critical assets. Critical assets referto those assets, which when disrupted critically impact plant operation.This can lead to manufacturing processes being compromised. Reducedproduct quality or even manufacturing stops may the result. In theworst-case scenario fire, explosion or toxic gas release may be theresult of such disruption. Hence such critical assets may require morerigorous monitoring and/or controlling then other assets depending onthe chemical processes and the chemicals involved.

Additionally or alternatively the second processing layer may beconfigured to provide data to an external network e.g. via an interfaceto an external network. The second processing layer may becommunicatively coupled to an external processing layer via an externalnetwork. Adding stacked processing layers in or inside the securenetwork allows to comply with high safety standards in chemicalindustry. In particular such architecture allows the method of beingperformed fully independent of the external management layer enabling anisland mode for one or more chemical plants. Here an island mode refersto monitoring and/or controlling of chemical plants without access to anexternal network.

In a further aspect the first processing layer is configured to provideasset or process specific data and the second processing layer isconfigured to provide plant specific data. The second processing layermay be configured to contextualize asset or process specific data. Thisway the performance of the first processing layer is not affected. Sincetypical core process systems of the first processing layer particularlyin older plants do not have the required computing power, adding afurther system with higher performance even enables contextualization.Additionally the second processing layer allows for datacontextualization on a plant level rather than an asset level. Datacontextualization in the present context relates to adding contextinformation to asset or process specific data or to reducing the datasize by pre-processing asset or process specific data. Adding contextmay include adding further information tag(s) to the asset or processspecific data. Pre-processing may include filtering, aggregating,normalizing, averaging, or inference of asset or process specific data.

In one aspect the first processing layer is associated with one or asingle chemical plant. The first processing layer may be a core processsystem including one or more processing devices and storage devices.Such layer may include one or more distributed processing and storagedevices forming a programable logic controller (PLC) system ordecentralized control system (DCS) with control loops distributedthroughout the chemical plant. Preferably the first processing layer isconfigured to control and/or monitor chemical processes and assets onthe asset level. Hence the first processing layer monitors and/orcontrols the chemical plant on the lowest level. Furthermore, the firstprocessing layer may be configured to monitor and control criticalassets. Additionally or alternatively, the first processing layer isconfigured to provide process or asset specific data to the secondprocessing layer. Such data may be provided directly or indirectly tothe second processing layer.

In a further aspect the second processing layer is associated with morethan one chemical plant. The second processing layer may include aprocess management system with one or more processing and storagedevices. Preferred the second processing layer or the process managementsystem is configured to manage data transfer to and/or from the firstprocessing layer. Further preferred the second processing layer or theprocess management system is configured to host and/or orchestrateprocess applications. Such process applications may monitor and/orcontrol one or more chemical plant(s) or one or more asset(s). Theprocess management system may be associated with one or more chemicalplants. In other words, the process management system may becommunicatively coupled to multiple first processing layers associatedwith one or more chemical plant(s).

In a further aspect the second processing layer may comprise anintermediate processing system and a process management system. Here theintermediate processing system may be communicatively coupled to thefirst processing layer, preferably the core process system, and theprocess management system may be communicatively coupled to theintermediate layer. Preferably the first processing layer and theprocess management system are coupled or communicatively coupled via theintermediate processing system. The intermediate processing system maybe configured to collect process or asset specific data provided by thefirst processing layer. The process management system may be configuredto provide plant specific data of one or more chemical plant(s) to theinterface to the external network. The intermediate processing systemmay be associated with one or more chemical plants. In other words, theintermediate processing system may be communicatively coupled to firstprocessing layer of one chemical plant or to multiple first processinglayers of multiple plants. The process management system may becommunicatively coupled to one or multiple intermediate processingsystems. Adding the intermediate processing level to the secondprocessing layer adds a further security layer. It fully detangles thevirulent first processing layer from any external network access.Additionally, the intermediate level allows for more enhanced datahandling by reducing data transfer rates to the external processinglayer via pre-processing and enhancing data quality bycontextualization. The intermediate processing system and processmanagement system may comprise one or more processing and storagedevices.

In a further aspect the secure network is a segregated network includingmore than two security zones separated by firewalls. Such firewalls maybe network or host-based virtual or physical firewalls. The firewall maybe hardware- or software-based to control incoming and outgoing networktraffic. Here predetermined rules in the sense of a white listing maydefine allowed traffic via access management or other configurationsettings. Depending on the firewall configuration the security zones mayadhere to different security standards.

In a further aspect the first processing layer is hosted, situated orconfigured in or inside a first security zone via a first firewall andthe second processing layer is hosted, situated or configured in orinside a second security zone via a second firewall. To securely protectthe first processing layer, the first security level may adhere to ahigher security standard than the second security level. Security levelsmay adhere to a common industry standard such as lined out in Namurdocumentation IEC 62443. The second processing layer may provide furthersegregation via security zones. For example, the intermediate processingsystem may be hosted, situated or configured in or inside a thirdsecurity zone via a third firewall and the process management system maybe configured in the second security zone via the second firewall. Thethird and second security zones may be staggered in terms of securitystandard as well. For instance the third may adhere to a securitystandard higher than the second. This allows for higher securitystandards on the lower security zone of the first processing layer andlower security standards on higher security zones of the secondprocessing layer. In one embodiment the first processing layer is insidea first security zone, the process management system is inside thesecond security zone and the intermediate processing system is inside athird security zone.

The second processing layer may be configured to contextualize processor asset specific data. This way the performance of the first processinglayer is not affected. Since typical core process systems in olderplants do not have the required computing power, adding a further systemwith higher performance even enables contextualization. Additionally thesecond processing layer and in particular the intermediate processingsystem allows for data contextualization on a plant level rather than anasset level. Data contextualization in the present context relates toadding context information to process or asset specific data or toreducing the data size by pre-processing process or asset specific data.Adding context may include adding further information tag(s) to theprocess or asset specific data. Pre-processing may include filtering,aggregating, normalizing, averaging, or inference of process or assetspecific data.

In further aspect unidirectional or bidirectional communication, e.g.data transfer or data access, may be realized for data streams betweendifferent processing layers. In other words the system may be configuredto allow for unidirectional or bidirectional communication, e.g. datatransfer or data access between different processing layers. One datastream may include process or asset specific data from the firstprocessing layer being passed to and contextualized via the secondprocessing layer and communicated to the external processing layer.Contextualization may be performed on the second processing layer, theexternal processing layer or both. In other words, the second processinglayer, the external processing layer or both may be configured tocontextualize process or asset specific data or plant specific data.Furthermore, depending on criticality of the process or asset specificdata or the plant specific data such data may be assigned forunidirectional or bidirectional communication. In other words, thesystem may be configured to assign unidirectional or bidirectionalcommunication to process or asset specific data or the plant specificdata depending on criticality of the process or asset specific data orthe plant specific data. E.g. data communication from the second orexternal processing layers to critical assets may be prohibited byrealizing a diode type communication channel. Such communication mayonly allow for unidirectional communication from the critical asset tothe processing layers but not vice versa.

In further aspect data streams may be assigned critical or non-criticaldata. In other words, the system may be configured to assign critical ornon-critical data tags. Critical data refers to data that is critical tooperate the chemical plant, such as short-term data, from whichoperation points of the chemical plant are derived. Such critical datamay cover a short term horizon of, e.g. hours or days up one or moreweek(s), which is required to operate the plant in its optimal state.Non-critical data refers to data that is not critical to operate thechemical plant, such as mid- to long-term data for monitoring thechemical plant based on mid- to long-term behavior. Such non-criticaldata may cover a mid- to long-term time horizon, e.g. multiple weeks ormonths up to one or more year(s), which is required to monitor and/orcontrol asset(s) or plant(s) e.g. over a time span. Such data may alsobe referred to as cold, warm and hot data, wherein the hot datacorresponds to critical data, the warm data corresponds to mid-termnon-critical data and cold data corresponds to long-term non-criticaldata.

In a further aspect data contextualization is staggered across systemlayers, processing layers or processing systems included in suchprocessing layers with each layer mapping context information availablein the respective layer. In other words, the system may be configured tostagger data contextualization across system layers, processing layersor processing systems included in such processing layers with each layermapping context information available in the respective layer.Staggering may include contextualization of asset or process specificdata on different levels adding context information on single plantlevel and/or on multi plant level. In the layered system architecturecontext information available in one layer may be mapped to dataprovided by the lower layer or processing system. Here lower meanscloser to the chemical plant data access. For example, the process orasset specific data provided by the first processing layer may includecontext information on the asset level. In other words, the firstprocessing layer may be configured to provide the process or assetspecific data including context information on the asset level. Suchcontext information may relate to real time information such as ameasurement value, measurement quality, product quality, batch relateddata, or measurement time. Context information on asset level mayfurther relate to asset specific information such as an assetidentifier, intralogistics or measurement unit identifier. Theintermediate system and the process management system may be configuredto add further context information to or to contextualize such processor asset specific data. Such context information may relate to the plantlevel rather than the asset level. Context information for instancerelates to plant context such as plant identifier, plant type,reliability indicator, alarm limits, or application context such asmodel identifier, third party exchange identifier, confidentialityidentifier. This way the data quality can be enhanced to maximizecontext and with that data management and the resulting monitoringand/or controlling capabilities via process applications.

In a further aspect the intermediate processing system is configured tocontextualize data by mapping the heterogeneous process or assetspecific data to a homogeneous data format on plant level. In thiscontext heterogeneous refers to data that relates to the asset level ormultiple assets individually, while homogeneous data refers to data thatrelates to a combination or equal types of assets in a plant. Theintermediate processing system may be configured to provide such plantspecific data to the process management system. The process managementsystem may further be configured to contextualize the plant specificdata provided by the intermediate processing system preferably on multiplant level. Such contextualization may include adding contextinformation on a multiple plant level or on a site level such as multiplant or site context including technical asset structure of one ormultiple plant(s) or asset management information such as asset network.Additionally or alternatively such contextualization may include addingapplication context such as model identifier, third party exchangeidentifier or a confidentiality identifier.

Additionally or alternatively, the first processing layer may beconfigured to provide asset or process specific data to the secondprocessing layer. Such data may be provided directly or indirectly tothe second processing layer. The second processing layer may beassociated with one or more plant(s). The second processing layer mayinclude a process management system and optionally an intermediateprocessing layer. The first processing layer may include plant-specificcore process systems. The core process system(s) may be communicativelycoupled to the process management system optionally via the intermediateprocessing layer. The second processing layer, in particular the processmanagement system, and the external processing layer may be configuredto contextualize, store or aggregate data from one or more chemicalplant(s) and/or to orchestrate process models for one or more chemicalplant(s).

In a further aspect the intermediate processing system is configured tocontextualize data by mapping the heterogeneous process or assetspecific data to a homogeneous data format on plant level. In thiscontext heterogeneous refers to data that relates to the asset level ormultiple assets individually, while homogeneous data refers to data thatrelates to a combination or equal types of assets in a plant. Theintermediate processing system may be configured to provide such plantspecific data to the process management system. The process managementsystem may further be configured to contextualize the plant specificdata provided by the intermediate processing system preferably on multiplant level. Such contextualization may include adding contextinformation on a multiple plant level or on a site level such as multiplant or site context including technical asset structure of one ormultiple plant(s) or asset management information such as asset network.Additionally or alternatively such contextualization may include addingapplication context such as model identifier, third party exchangeidentifier or a confidentiality identifier.

The second processing layer, preferably the process management system,may be communicatively coupled to an external processing layer via anexternal network. The second processing layer, preferably the processmanagement system, may be configured to manage data transfer to and/orfrom the external processing layer in real time or on demand. The secondprocessing layer, preferably the process management system, may forinstance be configured to provide plant specific data to the interfaceto the external network based on an identifier added by way ofcontextualization. Such identifier may be a confidentiality identifierbased on which such data is not provided to the interface to theexternal network.

The external processing layer may be a computing or cloud environmentproviding virtualized computing resources, like data storage andcomputing power. The external processing layer may provide a private,hybrid, public, community or multi cloud environment. Cloud environmentsare advantageous, since they provide on demand storage and computingpower. Additionally, in cases where multiple chemical plants operated bydifferent parties are to be monitored and/or controlled, data or processapplications affecting the chemical plants may be shared in such cloudenvironment.

In a further aspect the second processing layer, preferably the processmanagement system, is configured to provide plant specific data from oneor more chemical plant(s) to the external processing layer. The secondprocessing layer, preferably the process management system, may befurther configured to delete at least parts of the data transferred tothe external processing layer. The external processing layer may beconfigured to store historical data from one or more chemical plant(s).The external processing layer may be configured to aggregate, store orcontextualize plant specific data from more than one chemical plantand/or to store historical data from more than one chemical plant. Hereaggregation means the grouping of data via an aggregate function like asum, an average or a mode. Aggregation hence relates to a function thatreduces dimension or storage space. This way data storage can beexternalized, and the required on-premise storage capacities can bereduced plus history transfer is made redundant. Furthermore, owing tothe flexible computing and storage resources of the external processinglayer and the fact that the data is available in the external processinglayer, process applications can be built, trained, tested, or modifiedin the external processing layer.

In a further aspect the second processing layer, preferably the processmanagement system, is configured to manage data transfer to and/or fromthe external processing layer in real-time or on demand. Real-timetransfer may be buffered depending on network and computing loads on theinterface to the external network. On demand transfer may be triggeredin a predefined or dynamic manner. Preferred the data transfer to theexternal processing layer is managed in real-time and the transfer fromthe external processing layer is managed on demand.

In a further aspect the second processing layer, preferably the processmanagement system, is configured to store or to manage access tohistorical data, real-time data and planning data. In a further aspectthe second processing layer, preferably the process management system,is configured to store or to manage access to historical data for afirst time window and the external processing layer is configured tostore historical data for a second time window, wherein the first timewindow is shorter than the second time window. Here the first timewindow may correspond to a critical time window allowing the system tomonitor and/or control the chemical plant in island mode withoutexternal network connection. The first time window may be viewed as ahot window, for which historical data is required to safely controland/or monitor the chemical plant. The first time window or hot windowmay be determined based on storage capacity of the second processinglayer, preferably the process management system, or preferably by theprocess applications and the historical data required to execute on theprocess applications in island mode without external network connection.This way availability of the system for monitoring and/or controlling isalways guaranteed.

In a further aspect deployment is managed by an orchestrationapplication that manages deployment of containerized applications basedon the input data, the load indicator, or the system layer tag.Additionally or alternatively the orchestration application is hosted bythe second processing layer and/or the external processing layer. In afurther aspect the orchestration application hosted by the secondprocessing layer on execution manages critical containerizedapplications. Additionally or alternatively the orchestrationapplication hosted by the external processing layer on execution managesnon-critical containerized applications. Such management on executionmay be assigned statically or dynamically. In a dynamic scenario, thesecond processing layer may host a back-up of critical containerizedapplications, and the orchestration application hosted by the secondprocessing system may access such back-up, if external net-workconnectivity is disrupted. Here critical containerized applicationsrefer to those containerized applications monitoring an/or controllingcritical assets. Such applications are hence required, if the monitoringand/or controlling needs to run in island mode.

In a further aspect the management of critical containerizedapplications is assigned to the second processing layer based on ahistory criterion reflecting a time window of available historical dataon the first or second processing layer. The second processing layer maybe configured to store historical aggregated data for a first timewindow and the external processing layer may be configured to storehistorical aggregated data for a second time window, wherein the firsttime window is shorter than the first time window. In a preferredembodiment the first time window is chosen such that criticalcontainerized applications may be executed on the first or secondprocessing layer. In a further aspect the containerized application isdeployed to execute on the second processing layer or the externalmanagement layer depending on a history criterion reflecting a timewindow of available historical data. In such embodiments the applicationmay be executed in the level such data is available. Hence no furtherdata transfer between processing layers is required reducingcommunication and processing loads. In combination with thecontextualization concept staggered across layers processing of plantspecific data on the first processing layer would introduce redundantdata transfers, once from the first processing layer to the second forcontextualization and back to the first processing layer for applicationexecution.

Deployment may depend on the input data. In a further aspect theassignment of deployment layer depends on a data availability indicator,a criticality indicator or a latency indicator.

The data availability indicator may relate to the input data ingested bythe containerized application. Based on such indicator execution may beassigned to deployment layers where the data is directly available orstored. For instance the first processing layer may be configured toprovide asset or process specific data and the second processing layermay be configured to provide plant specific data. A containerizedapplication ingesting asset or process specific data may be deployed onthe first processing layer. Similarly a containerized applicationingesting plant specific data may be deployed on the second processinglayer. Applications may be executed in the processing layer that hoststhe data to avoid redundant data transfers and reduce loads.

The criticality indicator may be a static or a dynamic indicator. Incase of a static indicator criticality of an asset, an asset group or aplant may be pre-defined. In case of a dynamic indicator criticalityindicator may be assigned dynamically depending on the output data ofprevious applications runs or other application runs. For instance, thecriticality indicator may be determined based on key performanceparameters of an asset such as its health status. If the health statusof an asset becomes critical over time, the criticality criterion may bechanged, and the containerized application may as a result of suchchange be run in a different deployment layer allowing for reducedlatency on e.g. data transfer. In an example the containerizedapplication ingests asset or process specific data of a specific assetand the criticality indicator signifies to be fulfilled, if execution isassigned to the second processing layer rather than the first processinglayer. If the health status of the specific asset changes and closermonitoring e.g. at higher frequency may be required, the criticalityindicator may be reset signifying the criticality indicator not beingfulfilled, if execution is assigned to the second processing layer. Insuch case the application may be assigned to the first processing layer.

The latency indicator may be a static or a dynamic indicator. In case ofa static indicator latency requirements of an asset, an asset group,part of a plant or a plant may be pre-defined. In case of a dynamicindicator latency requirements may be assigned dynamically dependent onsignatures of the input data. Such signatures may relate to a frequencyof change in real-time measurement data as e.g. derived from historicalreal-time measurement data. In an example a monitoring signal of a pumpmay show higher frequencies than a monitoring signal of a heatexchanger. In such a case the containerized application monitoring thepump may be deployed on the asset level either directly on the pumpcontroller or on the core process system of the respective plant. Fore.g. the pump the respective containerized application may be deployedin a processing layer as close to the pump as possible to reducelatency. The latency criterion may hence signify the time criticality ofthe containerized application.

The load indicator may be based on processing and/or network loads ofthe respective deployment layers. Additionally or alternativelyapplications may be executed on the deployment layer that providessufficient computing and storage resources to reduce the processing loadon other processing layers and ensure critical application execution isnot impacted. In such cases the input data may be transferred to therespective deployment layer. This is particularly advantageous forapplications requiring high computing load or less time critical andhence allowing for data transfer latency.

In a further aspect the deployment may depend on a system layer tag thatis associated with the containerized application. The system layer tagmay be a configuration in the orchestration application, that e.g.deploys, executes and monitors the containerized application. In suchcase the containerized application may include an applicationidentifier, which the orchestration application may use foridentification on deployment. This way the deployment may be “hardwired” to ensure critical applications are executed in the right layer.Such deployment scheme may be particularly relevant to containerizedapplications monitoring and/or controlling critical assets. In thiscontext critical assets are those, which when disrupted criticallyimpact plant operation. Here the plant specific data refers tocontextualized asset or process specific data.

In a further aspect the containerized application is deployed tomultiple assets or plants of the same type. Assets of the same type mayrelate to assets with similar functionalities, from the same supplierand/or with similar characteristics, e.g. performance characteristics.Plants of the same type may relate to plants producing the similarintermediate or end product, having a similar physical asset structureor based on similar chemical processes. Here similar means comparable inthe sense that the behavior of the assets or plant show behavior thatdoes not deviate by more than an error tolerance or can be modelled by asingle model. Preferably the containerized application is associatedwith an asset or plant identifier. Such identifier may be aconfiguration setting of the orchestration application or thecontainerized application. The plant or asset identifier may be onedimensional signifying one plant or asset type or multi-dimensionalsignifying multiple plants or assets, for which the containerizedapplication is to be executed. In particular if the first processinglayer is with different processing units plant or even asset or processspecific such asset identifier allows to address assets associated withdifferent processing units at the same time allowing for highlyefficient deployment of containerized applications.

In a further aspect the containerized application is modified based onthe input data and the output data of containerized applicationsexecuted for multiple assets or plants of the same type. This may bedone regularly at per-defined times or dynamically. By aggregating suchdata the containerized application and particularly plant or assetmodels may be validated or improved in accuracy. This way the models canbe adapted to reflect the behavior of physical plants or assets. This isparticularly important for maintenance cycle or life cycle, since thephysical plants or assets may shift their behavior depending on thestage during such cycles. Such shifts may be compensated automated andself-optimized by the proposed system.

In a further aspect the containerized application is monitored based ona confidence level of the input data, the asset model or the plantmodel. The containerized application may provide such confidence levelsas output data. Respective operations may be embedded into thecontainerized application via the asset or plant models or via separatemodels. A confidence level on the input data may be generated byanalyzing e.g. patterns in real-time measurement data. A confidence onthe asset or plant model may be generated as part of the execution ofthe model operation based on input data.

In a further aspect an event signal is triggered, if the confidencelevel falls below a confidence a threshold, e.g. if confidence is below70%, 80% or 90%. Such event signal may indicate a failure of an asset oran operation of the application. If an anomalous pattern is detected onthe input data and the respective confidence level falls below athreshold application execution may be stopped and a failure signal maybe passed to a controlling instance, e.g. on the first processing layerof the chemical plant. Additionally or alternatively such failure signalmay be passed to a monitoring instance, e.g. on the first or secondprocessing layer or to a client application e.g. for display to anoperator. This way sensor failures and the need to retro-fit may bedetected.

In a further aspect modification of the asset or plant model istriggered, if the confidence level exceeds a threshold. Modification maybe triggered automatically. Such modification may include hardware orsoftware modifications. Modifications for instance include re-trainingof the asset or plant model based on historical data, adaption of theinput data channel, adaption of the scenario matrix or triggering anevent signal to maintain a hardware such as an Internet of Things (IoT)device.

In a further aspect the modification of the asset or plant model isperformed on the second processing layer, particularly the processmanagement system, or the external processing layer. Since suchmodifications do not interfere with the containerized application in thefirst place, it may be computed on the external processing layerreducing processing loads on monitoring and/or controlling on morecritical processing layers, such as the first and second processinglayers.

In a further aspect an external containerized application from athird-party environment is provided and deployed to execute on theexternal processing layer. Applications from third-party environmentmeans any application that was not created in the proprietary system offirst processing layer, second processing layer and external processinglayer. This way the risk of third party containerized applicationsinfecting the monitoring and/or control system of chemical plants isreduced.

In a further aspect creation of a new containerized application isperformed in the external processing layer. E.g. asset or plant modelsmay be trained on the external processing layer. Preferably the externalprocessing layer is configured to store aggregated data for more thanone chemical plant. The second processing layer may be configured toprovide aggregated data from one or more chemical plant(s) to theexternal processing layer. The second processing layer may be configuredto transfer aggregated data to the external processing layer inreal-time or on demand. The second processing layer may be configured todelete at least parts of the data transferred to the external processinglayer.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments of the present disclosure are illustrated in theappended drawings. It is to be noted, however, that the appendeddrawings illustrate only particular embodiments of the presentdisclosure and are therefore not to be considered limiting of its scope.The technical teaching may encompass other equally effectiveembodiments.

FIG. 1 shows a first schematic representation of the system formonitoring and/or controlling one or more chemical plant(s).

FIG. 2 shows a second schematic representation of the system formonitoring and/or controlling one or more chemical plant(s).

FIG. 3 shows a third schematic representation of the system formonitoring and/or controlling one or more chemical plant(s).

FIG. 4 shows a schematic representation of the data contextualizationconcept in systems like those shown in FIGS. 1 to 3 .

FIG. 5 shows a flowchart in a schematic representation of the method formonitoring and/or controlling one or more chemical plant(s).

FIG. 6 shows a schematic representation of systems for monitoring and/orcontrolling one or more chemical plant(s) via containerizedapplications.

FIG. 7 shows a flowchart in a schematic representation of the method formonitoring and/or controlling a chemical plant with multiple assets.

FIG. 8 shows a schematic representation of the system for monitoringand/or controlling more than one chemical plants in different securenetworks, which are configured for data and application transfer.

DETAILED DESCRIPTION

In petrochemicals process industrial production typically starts withupstream products, which are used to derive further downstream products.To date the value chain production via various intermediate products toan end product is highly restrictive and based on siloed infrastructure.This hampers introduction of new technologies such as IoT, cloudcomputing and big data analytics.

Unlike other manufacturing industries, process industry is subject tovery high standards in particular with regard to availability andsecurity. For this reason, computing infrastructures are typicallyunidirectional and siloed with highly restrictive access to monitoringand control systems of chemical plants.

In general chemical production plants are embedded in an enterprisearchitecture in a siloed way with different levels to make a functionalseparation between operational technology and information technologysolutions.

Level 0 relates to the physical processes and defines the actualphysical processes in the plant. Level 1 relates to intelligent devicesfor sensing and manipulating the physical processes, e.g. via processsensors, analyzers, actuators and related instrumentation. Level 2relates to control systems for supervising, monitoring and controllingthe physical processes. Real-time controls and software; DCS,human-machine interface (HMI); supervisory and data acquisition (SCADA)software are typical components. Level 3 relates to manufacturingoperations systems for managing production work flow to produce thedesired products. Batch management; manufacturing execution/operationsmanagement systems (MES/MOMS); laboratory, maintenance and plantperformance management systems, data historians and related middlewareare typical components. Time frames for controlling and monitoring maybe shifts, hours, minutes, seconds. Level 4 relates to businesslogistics systems for managing the business-related activities of themanufacturing operation. ERP is the primary system and establishes thebasic plant production schedule, material use, shipping and inventorylevels. Time frame may be months, weeks, days, shifts.

Additionally, such structures adhere to strict one-way communicationprotocols allowing for no data flow into level 2 or below. Not coveredin such architectures is the company or enterprise-external internet.The model remains, however, an essential concept within the realm ofCyber Security. Within this context, the challenge is to leverage thebenefits of Cloud computing and Big Data, while still guaranteeing theestablished advantages of existing architectures: i.e. the highavailability and reliability of the lower levels system (Level 1 andLevel 2), that control the chemical plant, as well as the cybersecurity.

The technical teaching presented here allows for enhancing monitoringand/or control changing this framework in a systematic way, to introducenew capabilities that are compatible with existing architectures. Thepresent disclosure specifically relates to a highly scalable, flexibleand available computing infrastructure for process industry, which atthe same time adheres to the high security standards.

FIG. 1 shows a first schematic representation of the system 10 formonitoring and/or controlling chemical plants 12.

The system 10 comprises two processing layers including the firstprocessing layer in the form of a core process system 14 associated witheach of the chemical plants 12 and the second processing layer 16, e.g.in the form of a process management system, associated with two chemicalplants 12. The core process system 14 is communicatively coupled to thesecond processing layer 16 allowing for unidirectional or bidirectionaldata transfer. The core process system 14 comprises a decentralized setof processing units associated with assets of the chemical plant 12.

The core process system 14 and the second processing layer 16 areconfigured in the secure network 18, 20, which in the schematicrepresentation includes two security zones. The first security zone issituated on the core process system 14 level, where the first firewall18 controls incoming and outgoing network traffic to and from the coreprocess system 14. The second security zone is situated on the secondprocessing layer 16, where the second firewall 20 controls incoming andoutgoing network traffic to and from the second processing layer 16.Such segregated network architecture allows to shield vulnerable plantoperations from cyberattacks.

The core process system 14 provides process or asset or process specificdata 22 of the chemical plant 12 to the second processing layer 16. Thesecond processing layer 16 is configured to contextualize the process orasset or process specific data of the chemical plants 12. The secondprocessing layer 16 is further configured to provide plant specific data24 of the chemical plants 12 to the interface 26 to the externalnetwork. Here the plant specific data may refer to contextualizedprocess or asset or process specific data.

Process or asset or process specific data may include value, quality,time, measurement unit, asset identifier. Via contextualization furthercontext such as plant identifier, plant type, reliability indicator, oralarm limits for the plant may be added. In a further step technicalasset structure of one or multiple plant(s) or a site and other assetmanagement (e.g. asset network), plus application context (e.g. modelidentifier, third party exchange) may be added.

The second processing layer 16 is communicatively coupled to an externalprocessing layer 30 via interface 26 to the external network. Theexternal processing layer 30 may be a computing or cloud environmentproviding virtualized computing resources, like data storage andcomputing power. The second processing layer 16 is configured to provideplant specific data 24 from one or more chemical plants 12 to theexternal processing layer 30. Such data may be provided in real time oron demand. The second processing layer 16 is configured to manage datatransfer to and/or from the external processing layer in real-time or ondemand. The second processing layer 16 may for instance provide plantspecific data 24 to the interface 26 to the external network based on anidentifier added by way of contextualization. Such identifier may be aconfidentiality identifier based on which such data is not provided tothe interface 26 to the external network. The second processing layer 16may be further configured to delete at least parts of the datatransferred to the external processing layer 30.

The external processing layer 30 is configured to aggregate plantspecific data from more than one chemical plant and/or to storehistorical data from more than one chemical plant. This way data storagecan be externalized, and the required on-premise storage capacities canbe reduced plus history transfer is made redundant. Additionally, suchstorage concept allows to store historical data on the second processinglayer 16 for a hot window, which is a critical time window allowing thesystem 10 to monitor and/or control the chemical plant in island modewithout external network connection. This way availability of the system10 for monitoring and/or controlling is always guaranteed.

The second processing layer 16 and the external processing layer 30 areconfigured to host and/or orchestrate process applications. Inparticular the second processing layer 16 may host and/or orchestrateprocess applications relating to core plant operations and the externalprocessing layer 30 may be configured to host and/or orchestrate processapplications relating to non-core plant operations.

Furthermore, the second processing layer 16 and the external processinglayer 30 may be configured to exchange data with 3rd party managementsystems, e.g. via integration of 3rd party external processing layer, toorchestrate data visualization, to orchestrate computing processworkflows, to orchestrate data calculations, to orchestrate APIs toaccess data, to orchestrate metadata of data storage, transfer andcalculation, to provide interactive plant data working environment forusers, e.g. operators and to verify and improve data quality.

FIG. 2 shows a second schematic representation of the system 10 formonitoring and/or controlling one or more chemical plant(s) 12.

The system 10 shown in FIG. 2 is similar to the system shown in FIG. 1 .However, the system of FIG. 2 has a second processing layer with aprocess management system 32 and an intermediate processing system 34.The intermediate processing systems 34.1, 34.2 is configured in asecurity zone of the secure network via firewall 40.

The intermediate processing systems 34.1, 34.2 may be configured toingest process or asset or process specific data 22 from individual ormultiple chemical plants 12. Such data is contextualized on a plantlevel in intermediate processing system 34.1, 34.2 and plant specificdata 38 may be provided to the process management system 32, wherefurther contextualization e.g. across plant levels on Verbund or sitelevel may be performed. In this setup the data contextualization isstaggered across the different system 10 layers with each layer 14, 34,32 mapping context information available in the respective layer 14, 34,32.

FIG. 3 shows a third schematic representation of the system 10 formonitoring and/or controlling one or more chemical plant(s) 12.

The system 10 shown in FIG. 3 is similar to the systems shown in FIGS. 1and 2 . However, the system of FIG. 3 includes monitoring devices 44,which are communicatively coupled to the process management system 32 orthe external processing layer 30. The monitoring device 36 may beconfigured to transfer monitoring data to process management system 32or the external processing layer 30. The process management system 32 orthe external processing layer 30 may be configured to manage multiplemonitoring devices 44. Since such IoT devices are not consideredreliable, monitoring data provided by the monitoring device 44 may betagged unidirectional, and any control loop relating to the chemicalplant 12 may include a filter for such tag. Thus, such data will not beused for any control of the chemical plant 12.

FIG. 4 shows a schematic representation of the data contextualizationconcept in systems 10 like those shown in FIGS. 1 to 3 .

The systems 10 of FIGS. 1 to 3 include two internal processing layers14, 16, 32, 34 and the external processing layer 30. The firstprocessing layer 14 may be a decentralized control system forsupervising, monitoring and controlling the physical processes in thechemical plant 12. The first processing layer 14 may be configured toprovide process or asset or process specific data. The second processinglayer 16, 32, 34 may include the intermediate processing system 34 andthe process management system 32. The intermediate processing system 34may be configured as an edge computing layer. Such layer may beassociated to Level 3 for individual plants. The intermediate processingsystem 34 may be configured for

-   -   collecting process or asset or process specific data,    -   interaction with basic automation systems from Level 2,    -   initial contextualization (bottom-up approach), wherein context        is added based on what is known on Level 2 and Level 1 and        within the decentralized edge device,

The process management system 32 may be configured as centralized edgecomputing layer. Such layer may be associated to Level 4 for multipleplants. The process management system 32 may be configured for:

-   -   integration of data from different decentralized edge devices        including the intermediate processing system 34 or monitoring        devices 44,    -   further contextualization (bottom-up approach), wherein        additional context is added based on preprocessed context in the        decentralized within the decentralized edge devices.

The external processing layer 30 may be configured as centralized cloudcomputing platform. Such platform may be associated with Level 5 formultiple plants. The external processing layer 30 may be configured asmanufacturing data workspace with full data integration across multipleplants including manufacturing data history transport & streaming,collection of all data from all edge components. This way the fullcontextualization of all lower level context may be integrated in the onthe external processing layer 30 for multiple plants. Thus, the externalprocessing layer 30 may be further configured to

-   -   run cloud-native apps,    -   connect with external PaaS and SaaS tenants,    -   integrate machine learning with manufacturing data & processes,        train-test-deploy, visualize data, access apps, orchestrate.

By way of system architecture, a bottom-up contextualization concept maybe realized. Such concept is shown in FIG. 4 . In the bottom-up conceptall information that is available on the lower-levels may already beadded to the data as attributes, such that lower level context is notlost. Here the first processing layer 14 as the lowest context level mayinclude measurement values 11, which are contextualized with respect tothe item 13 the measurement was conducted with. The intermediateprocessing system 34 may further contextualize by adding further tags 15relating to the individual chemical plant 12. The process managementsystem 32 may further contextualize by adding tags 17 relating tomultiple chemical plants 12 and/or business information. The externalprocessing layer 30 may further contextualize by adding tags 19 relatingto multiple plants and/or external context information, e.g. from thirdparties.

The contextualization concept may cover at least two fundamental typesof context. One type may be the functional location within theproduction environment comprising multiple chemical plants. This maycover information about what and where this data point represents insidethe production environment. Examples are the connection with afunctional location, an attribute with respect to which physical assetthe data is collected, etc. This context may be beneficially used forlater applications, since it explains which data is available for whichplants and assets.

Another type may be confidentiality categorization. Such tag may beadded on the lowest level possible and this information may bepropagated to further processing layers. Such tag may be addedautomatically or manually. With technical measures e.g. via a filterembedded into the firewalls, it may be prohibited automatically, that“strictly confidential” data is integrated all the way up to theexternal processing layer 30. Sharing of data with externals will leadto an automatic notification that “confidential data” is being shared.An automatic contractual check may be implemented to see whether thisdata can be shared with this external.

Overall the contextualization concept realized in such way allows forhighly efficient data usage in process applications deployed on anylayer of the system.

FIG. 5 shows a flowchart in a schematic representation of the method formonitoring and/or controlling one or more chemical plant(s).

Preferably the method is performed on a distributed computing system asshown in FIGS. 1 to 3 comprising a first processing layer 14 associatedwith the chemical plant 12 and communicatively coupled to a secondprocessing layer 16, 32, 34. The method may perform all steps asdescribed in the context of FIGS. 1 to 4 , including any steps relatingto contextualization, data handling, process application management andmonitoring device management.

In a first step, 61, process or asset or process specific data of thechemical plant 12 is provided via the first processing layer 14 to thesecond processing layer 16, 32, 34.

In a second step 63, process or asset or process specific data iscontextualized via the second processing layer 16, 32, 34 to generateplant specific data.

In a third step, 65, plant specific data of one or more chemicalplant(s) 12 is provided via the second processing layer 16, 32, 34 tothe interface 26 to the external network.

In a fourth step, 67, one or more chemical plant(s) are monitored and/orcontrolled via the second processing layer 16, 32, 34 or the firstprocessing layer 14 based on the process or asset or process specificdata or the plant specific data. Monitoring and/or controlling of theone or more chemical plant(s) 12 may be conducted via the secondprocessing layer 16, 32, 34 or the external processing layer 30 based onthe plant specific data. Additionally, monitoring and/or controlling maybe conducted via the first processing layer 14 based on the process orasset or process specific data. Such monitoring and/or controlling maybe performed through process applications ingesting respective data andproviding monitoring and/or controlling output as further lined out inFIGS. 6 to 8 .

FIG. 6 shows a schematic representation of the distributed computingsystem for monitoring and/or controlling one or more chemical plant(s)with multiple assets via a distributed computing system 10 with morethan two deployment layers 14, 16, 30.

The schematic of FIG. 6 represents containerized applicationorchestration in different deployment layers 14, 16, 30. The system 10includes an external processing system 30, a second processing layer 16and a first processing layer 14. Here the second processing layer 16 mayinclude larger storage and computing resources than the first processinglayer 14, and/or the external processing layer 30 may include largerstorage and computing resources than the second processing layer 16. Thesystem's 10 architecture and functionalities may adhere to thearchitectures and functionalities described with respect to FIGS. 1 to 3. In particular the first and the second processing layer 14, 16 may beconfigured in a secure network 20, 40, 18. The first processing layer 14may be communicatively coupled to the second processing layer 16 and thesecond processing layer 16 may be communicatively coupled to theexternal processing layer 30 via an external network 24.

The orchestration applications 56, 58 may be hosted by the externalprocessing layer 30 and the second processing layer 16, 32, 34respectively. Hence containerized applications or container images 48,50 may be stored in a registry of the external processing layer 30 andthe second processing layer 16, 32, 34 respectively. The containerizedapplications 48, 50 for execution may include one or more operations toingest input data, to provide the input data to respective asset orplant model(s) generating output data and to provide the generatedoutput data for controlling and/or monitoring the chemical plant 12.This way the external processing layer 30 and the second processinglayer 16, 32, 34 act as facilitating layers reducing the computing andstorage resources required on the first processing layer 14 on the assetlevel.

FIG. 7 shows a flowchart in a schematic representation of the method formonitoring and/or controlling a chemical plant 12 with multiple assetsvia a distributed computing system 10 as it may be performed in thesystems 10 shown in FIGS. 1 to 4 .

In a first step 60, the containerized application 48, 50 including anasset or plant template specifying input data, output data and an assetor plant model is provided. The containerized application 48. 50 may becreated on the external processing layer 30 or may be modified on thesecond processing layer 30. An external containerized application from athird party environment may be provided.

In a second step 62, the containerized application 48, 50 is deployed toexecute on at least one of the deployment layers 30, 32, 16, 34, 14wherein the deployment layer 30, 32, 16, 34, 14 is assigned based on theinput data, a load indicator, or a system layer tag, and thecontainerized application 48, 50 may be executed on the assigneddeployment layer(s) 30, 32, 16, 34, 14 to generate output data forcontrolling and/or monitoring the chemical plant 12. Deployment may bemanaged by an orchestration application 56, 50 that manages deploymentof containerized applications 48, 50 based on the input data, the loadindicator, or the system layer tag. The orchestration application may behosted by the second processing layer 16, 23, 34 and/or the externalprocessing layer 30. The orchestration application 56, 58 hosted by thesecond processing layer 16, 32, 34 manages critical containerizedapplications 48, 50, wherein the orchestration application 56, 58 hostedby the external processing layer 30 may manage non-criticalcontainerized applications 48, 50. The assignment of the deploymentlayer 30, 32, 34, 16, 14 may be based on input data depends on a dataavailability indicator, a criticality indicator or a latency indicator.A containerized application from a third party environment may bedeployed to execute on the external processing layer 30.

The orchestration applications 56, 58 may be hosted by the externalprocessing layer 30 and the second processing layer 16 respectively. Theorchestration applications 56, 58 may deploy containerized applications48, 50 on any deployment layer 30, 16, 14. The containerizedapplications 48, 50 may then be executed on respective deployment layer30, 16, 14 by running the process applications 46, 52, 54 in asandbox-type environment. The deployment layer 30, 16, 14 may beassigned based on the input data, the load indicator, or the systemlayer tag. For instance, management of critical containerizedapplications 50 may be assigned to the second processing layer 16optionally based on a history criterion reflecting a time window ofavailable historical data on the first or second processing layer 16.Advantageously the containerized applications 48, 50 may be deployed tomultiple assets or plants of the same type. Furthermore, thecontainerized applications 50, 48 may be modified based on the inputdata and the output data provided by containerized applications 46, 52,54 executed for multiple assets or plants of the same type.

In a third step 64 the containerized application 48, 50 may be monitoredbased on a confidence level of the input data, the asset model or theplant model during or after each execution. Based on the resultingconfidence level an event signal or modification of the asset or plantmodel may be triggered. Such Trigger may be set, if the confidence levelexceeds a threshold. Such threshold may be pre-defined or dynamic. If atrigger is set, the modification of the asset or plant model may beperformed e.g. on the second processing layer 16, 32, 34 or the externalprocessing layer 30.

In a fourth step 66 the generated output data is provided forcontrolling and/or monitoring the chemical plant 12. Such output datamay be passed to a persistent instance after execution of thecontainerized application 48, 50. In particular such output data may bepassed to a controlling instance, e.g. on the first processing layer 14of the chemical plant 12. Additionally or alternatively such output datamay be passed to a monitoring instance on the first processing layer 14,the second processing layer 16, 32, 34 or the external processing layer30. The output data may be passed to e.g. a client application fordisplay to an operator or a further containerized application 48, 50 forexecution.

FIG. 8 shows a schematic representation of systems 10.2, 10.2 formonitoring and/or controlling more than one chemical plants 12.1, 12.2in different secure networks 20.1, 20.2, which are configured for dataand process application transfer. FIG. 8 shows systems 10 of FIGS. 1 to3 including first and second processing layers 14, 16, 32, 34 and theexternal processing layer 30 as examples. Any other system architecturemay be similarly suited for process application and data transfer. Bothsystems are associated with separate secure networks 20.1, 20.2 andcommunicatively coupled to an external network 24.1, 24.2 via interfaces26.1, 26.2.

The systems 10.1, 10.2 are configured to exchange process or asset orprocess specific data or the process application based on the transfertag. By adding the transfer tag on the earliest level possible, i.e.where the data or the application is generated or first enters thesystem, the transfer tag becomes an inherent part of any data point orapplication as soon as the tag is added and follows the data orapplication on its path through the system 10.1, 10.2. Such transfer tagenables seamless, but secure integration of external data sources orexternal applications as well as transfer of data or application toexternal resources.

In one case shown in FIG. 8 an application 48 is exchanged between thesystems 10.1, 10.2. In this example the containerized application 48 istransferred via the external processing layer 30.1, 30.2 communicativelycoupled to the two systems 10.1, 10.2. Here the external processinglayer 30.1 is communicatively coupled to system 10.1 and the externalprocessing layer 30.2 is communicatively coupled to system 10.2. Theexchange of the containerized application 48 is performed indirectlythrough the external processing layers 30.1, 30.2. The containerizedapplication is tagged with a transfer tag including two transfersettings relating to confidentiality settings and/or third-partytransfer settings. This way the transfer may be prohibited based acompliance check on the external processing layer 30.2, e.g. if atransfer with respective third-party identifier is not associated withthird party identifier stored in a database of allowed third partytransfers for the process application 48. Similarly process or asset orprocess specific data may be transferred 72 between the systems 10.1,10.2. Any transfer between the systems 10.1, 10.2 may then be followedby further transfers from the external processing layer 30.1, 30.2 tothe respective system 10.1, 10.2.

Additionally, such transfer based on a transfer tag may be conducteddirectly between the systems 10.1, 10.2 between processing layers 32, 16associated with the secure networks 20.1, 20.1. Such transfers based ontransfer tag may be realized via a secure connection 74 between suchlayers 16, 23, such as a VPN connection. Any transfer between thesystems 10.1, 10.2 may then be followed by further transfers betweensystem components inside the secure networks 20.1, 20.2 or to theexternal processing layer 30.1, 30.2 of the respective system 10.1,10.2. By attaching the transfer tag to any data point and processapplication, containerized or not, allows to securely handle third-partytransfers between systems 10.1, 10.2 in separate secure networks 20.1,20.1.

Any of the components described herein used for implementing the methodsdescribed herein may be in a form of a distributed computer systemhaving one or more processing devices capable of executing computerinstructions. Components of the computer system may be communicativelycoupled (e.g., networked) to other machines in a local area network, asecure network, an intranet, an extranet, or the Internet. Components ofthe computer system may operate as a peer machines in a peer-to-peer (ordistributed) network environment. Parts of the computer system may be avirtualized cloud computing environment, edge gate ways, web appliances,servers, network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, it is to be understoodthat the terms “computer system,” “machine,” “electronic circuitry,” andthe like are not necessarily limited to a single component, and shall betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

Some or all of the components of such a computer system may be utilizedby or illustrative of any of the components of the system 10. In someembodiments, one or more of these components may be distributed amongmultiple devices or may be consolidated into fewer devices thanillustrated. Furthermore, some components may refer to physicalcomponents realized in hardware and others may refer to virtualcomponents realized in software on remote hardware.

Any processing layer may include a general-purpose processing devicesuch as a microprocessor, microcontroller, central processing unit, orthe like. More particularly, the processing layers may include a CISC(Complex Instruction Set Computing) microprocessor, RISC (ReducedInstruction Set Computing) microprocessor, VLIW (Very Long InstructionWord) microprocessor, or a processor implementing other instruction setsor processors implementing a combination of instruction sets. Theprocessing layer may also include one or more special-purpose processingdevices such as an ASIC (Application-Specific Integrated Circuit), anFPGA (Field Programmable Gate Array), a CPLD (Complex Programmable LogicDevice), a DSP (Digital Signal Processor), a network processor, or thelike. The methods, systems and devices described herein may beimplemented as software in a DSP, in a micro-controller, or in any otherside-processor or as hardware circuit within an ASIC, CPLD, or FPGA. Itis to be understood that the term “processing layer” may also refer toone or more processing devices, such as a distributed system ofprocessing devices located across multiple computer systems (e.g., cloudcomputing), and is not limited to a single device unless otherwisespecified.

Any processing layer may include suitable data storage device like acomputer-readable storage medium on which is stored one or more sets ofinstructions (e.g., software) embodying any one or more of themethodologies or functions described herein. The instructions may alsoreside, completely or at least partially, within the main memory and/orwithin the processor during execution thereof by the computer system,main memory, and processing device, which may constitutecomputer-readable storage media. The instructions may further betransmitted or received over a network via a network interface device.

A computer program for implementing one or more of the embodimentsdescribed herein may be stored and/or distributed on a suitable medium,such as an optical storage medium or a solid state medium suppliedtogether with or as part of other hardware, but may also be distributedin other forms, such as via the internet or other wired or wirelesstelecommunication systems. However, the computer program may also bepresented over a network like the World Wide Web and can be downloadedinto the working memory of a data processor from such a network.

The terms “computer-readable storage medium,” “machine-readable storagemedium,” and the like should be taken to include a single medium ormultiple medium (e.g., a centralized or distributed database, and/orassociated caches and servers) that store the one or more sets ofinstructions. The terms “computer-readable storage medium,”“machine-readable storage medium,” and the like shall also be taken toinclude any transitory or non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the present disclosure. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, optical media, and magnetic media.

Some portions of the detailed description may have been presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is herein, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the preceding discussion,it is appreciated that throughout the description, discussions utilizingterms such as “receiving,” “retrieving,” “transmitting,” “computing,”“generating,” “adding,” “subtracting,” “multiplying,” “dividing,”“selecting,” “optimizing,” “calibrating,” “detecting,” “storing,”“performing,” “analyzing,” “determining,” “enabling,” “identifying,”“modifying,” “transforming,” “applying,” “extracting,” and the like,refer to the actions and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

It has to be noted that embodiments of the invention are described withreference to different subject matters. In particular, some embodimentsare described with reference to method type claims whereas otherembodiments are described with reference to the system type claims.

However, a person skilled in the art will gather from the above and thefollowing description that, unless otherwise notified, in addition toany combination of features belonging to one type of subject matter alsoany combination between features relating to different subject mattersis considered to be disclosed with this application. However, allfeatures can be combined providing synergetic effects that are more thanthe simple summation of the features.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or example and not restrictive; theinvention is not limited to the disclosed embodiments. Other variationsto the disclosed embodiments can be understood and effected by thoseskilled in the art and practicing the claimed invention, from a study ofthe drawings, the disclosure, and the appended claims. In someinstances, well-known structures and devices are shown in block diagramform, rather than in detail, in order to avoid obscuring the presentdisclosure.

In the claims, the word “comprising” does not exclude other elements orsteps, and the indefinite article “a” or “an” does not exclude aplurality. A single processor or controller or other unit may fulfil thefunctions of several items recited in the claims. The mere fact thatcertain measures are recited in mutually different dependent claims doesnot indicate that a combination of these measures cannot be used toadvantage. Any reference signs in the claims should not be construed aslimiting the scope.

1. A method for monitoring and/or controlling a chemical plant (12) withmultiple assets via a distributed computing system (10) with more thantwo deployment layers (14, 16, 30, 32, 34), wherein the deploymentlayers (14, 16, 30, 32, 34) comprise at least two of a first processinglayer (14), a second processing layer (16, 32, 34) and an externalprocessing layer (30), the method comprising: providing (60) acontainerized application (48, 50) including an asset or plant templatespecifying input data, output data and an asset or plant model,deploying (62) the containerized application (48, 50) to execute on atleast one of the deployment layers (14, 16, 30, 32, 34), wherein thedeployment layer (14, 16, 30, 32, 34) is assigned based on the inputdata, a load indicator, or a system layer tag, and executing thecontainerized application (46, 52, 54) on the assigned deploymentlayer(s) (14, 16, 30, 32, 34) to generate output data for controllingand/or monitoring the chemical plant (12), providing (66) the generatedoutput data for controlling and/or monitoring the chemical plant (12).2. The method of claim 1, wherein the second processing layer (16, 32,34) includes larger storage and computing resources than the firstprocessing layer (14), and/or the external processing layer (30)includes larger storage and computing resources than the secondprocessing layer (16, 32, 34).
 3. The method of claim 1, wherein thefirst and the second processing layer (14, 16, 32, 34) are configuredinside a secure network (20), wherein the first processing layer (14) iscommunicatively coupled to the second processing layer (16, 32, 34) andthe second processing layer (16, 32, 34) is communicatively coupled tothe external processing layer (30) via an external network.
 4. Themethod of claim 1, wherein the containerized application (48, 50) forexecution includes one or more operations to ingest input data, toprovide the input data to respective asset or plant model(s) generatingoutput data and to provide the generated output data for controllingand/or monitoring the chemical plant (12).
 5. The method of claim 1wherein deployment is managed by an orchestration application (56, 58)that manages deployment of containerized applications (48, 50) based onthe input data, the load indicator, or the system layer tag.
 6. Themethod of claim 5, wherein the orchestration application (56, 58) ishosted by the second processing layer (16, 32, 34) and/or the externalprocessing layer (30).
 7. The method of claim 5, wherein theorchestration application (58) hosted by the second processing layer(16, 32, 34) manages critical containerized applications (48, 50),wherein the orchestration application (56) hosted by the externalprocessing layer (30) manages non-critical containerized applications(48, 50).
 8. The method of claim 5, wherein the management of criticalcontainerized applications (56, 58) is assigned to the second processinglayer (16, 32, 34) based on a history criterion reflecting a time windowof available historical data in the first or second processing layer(14, 16, 32, 34).
 9. The method of claim 1, wherein the assignment ofthe deployment layer (14, 16, 30, 32, 34) based on input data depends ona data availability indicator, a criticality indicator or a latencyindicator.
 10. The method of claim 1, wherein the containerizedapplication is deployed to multiple assets or plants of the same type.11. The method of claim 1, wherein the containerized application (48,50) is modified based on the input data and the output data provided bycontainerized applications (48, 50) executed for multiple assets orplants (12) of the same type.
 12. The method of claim 1, wherein thecontainerized application (48, 50) is monitored based on a confidencelevel of the input data, the asset model or the plant model
 13. Themethod of claim 12, wherein an event signal or a modification of theasset or plant model is triggered, if the confidence level falls below aconfidence threshold.
 14. The method of claim 12, wherein themodification of the asset or plant model is performed on the secondprocessing layer (15, 32, 34) or the external processing layer (30). 15.The method of claim 1, wherein an external containerized applicationfrom a third-party environment is provided and, deployed to execute onthe external processing layer (30).
 16. A system (10) for monitoringand/or controlling a chemical plant (12) with multiple assets with morethan two deployment layers (14, 16, 30, 32, 34), wherein the deploymentlayers (14, 16, 30, 32, 34) comprise at least two of a first processinglayer (14), a second processing layer (16, 32, 34) and an externalprocessing layer (30), the system (10) being configured to: provide (60)a containerized application (48, 50) including an asset or planttemplate specifying input data, output data and an asset or plant model,deploy (62) the containerized application (48, 50) to execute on atleast one of the deployment layers (14, 16, 30, 32, 34), wherein thedeployment layer (14, 16, 30, 32, 34) is assigned based on the inputdata, a load indicator, or a system layer tag, and executing thecontainerized application (46, 52, 54) on the assigned deploymentlayer(s) (14, 16, 30, 32, 34) to generate output data for controllingand/or monitoring the chemical plant (12), provide (66) the generatedoutput data for controlling and/or monitoring the chemical plant (12).